What cross-validation to use on this data set, when selecting best model

Question

Dataset:

structure(list(V1 = c(1.43359910241166, 0.411971077467806, 0.236361845246534, 
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1.55763841132305, 0.306848974278087, -1.06336757529454, 0.208462982445177, 
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0.609577869238014, -0.13720552696616, 0.920650581183744, 1.18282854178987, 
-0.792945405521446, -0.609722647650392, -0.21688852962299, -3.06426186175807, 
0.5498848363865), V2 = c(-0.322161064448354, -0.203202315321523, 
-1.37681357972322, -2.09183896169083, -1.73416522569493, -0.167163678879473, 
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1.05504527675313, 1.72684177309, 0.487583880103759, 0.366982237506534, 
0.341207392409481, 0.0878011635613361), V3 = c(-3.06259779143185, 
0.113156471002083, -0.596111339640452, -0.0549465535711572, -0.941898864240695, 
-0.653015082018507, -0.169956676284042, -0.35411953808696, 0.713862293279259, 
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0.0710046563366782, 0.701388784100771, 1.56870481640847, -0.841113890934613, 
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0.0627972059134885, -0.655416452787133, 0.589188711953821), V5 = c(-1.79836984688233, 
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0.55419775558349, -0.458225457879011, -2.14861215865916), V6 = c(-0.18026818728965, 
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1.34981730088023, 0.0611706096060544, 0.83874651540465, 0.58899516399665, 
1.24066391945654, -1.08080170411743, 0.597620326597847, -1.21365483260366, 
0.230893469563153, -0.576677068566099, 1.31703258659203, 0.35136844419016, 
0.925208426922233, 1.73348977742475, 0.514617170610343), V7 = c(0.692646184527114, 
1.64958468445801, -0.722861261417701, -0.411292490473929, -1.73926867251488, 
0.479847732965793, 0.224291785874008, -0.650661070391403, -0.20779505689401, 
-0.900990363217965, 0.712570690351891, 0.0291624484927884, 0.613871305452367, 
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1.00941096184201, -1.07148389565784, -0.439790917550134, -0.786567592396622, 
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0.279536175230915, -0.860839531512879, 1.62382620633742, -0.656202640703168, 
-3.05801703213563, 0.243884147081474, 0.926579301241956, 0.58184138659717, 
-0.0814078168437784, -0.0668035158044736, 0.00153834639170001, 
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0.758090513962191, 0.456430947715887, -1.73160959029873, 0.179898464937389
), V9 = c(2.56974590352874, -0.263155790779132, 0.646658371629822, 
-0.752843366448987, 0.200047856906594, 0.659371008337854, 1.24620285734473, 
0.94634794321528, -1.3304334794271, 1.33090401796431, -0.819840444239054, 
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0.62253221198192, 0.622634591405887, -0.547608828939565, 0.786557248787584, 
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0.0970193660300894, 1.18927210039089, -0.6957686086705, -0.386785336508124, 
-0.35257548033064, 2.31937096293864, -0.549132531058022, -0.0974568592721698, 
1.43853645612397, -0.0316945106071529), V11 = c(-1.86095070927053, 
0.573330283491408, -1.03183858717977, -1.83745190916475, -0.077180684913356, 
-0.94533768863225, -0.641638632478328, 0.154349543995556, 1.89664953662371, 
1.3494700201932, 1.04343452008192, 1.03948878970461, 0.394740150081754, 
1.24869842481551, 0.33270007318232, 0.373677276693529, 0.670774298645023, 
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-0.527741614949631, -1.55637393322463, -0.0817683516977811, 0.225671587747989
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0.441692874434554, 0.0707526888389656, 0.332161357520943, 0.0735800395703528, 
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-1.65064484659245), V13 = c(-1.97412125867671, 0.44572205242864, 
-0.274712915255066, -1.44692140049933, -1.18035700830368, -0.260286573948736, 
-0.95815595797825, -0.242760674716397, 0.477953228907608, 0.992878959448502, 
0.48518262700317, -0.882424015844636, 2.03856721097186, 0.782640940939034, 
0.00789969362112054, -0.295894328060507, 1.27922468162261, 0.51472928905797, 
0.0447383908218823, 0.165638463053774, -0.263332324321804, -1.15204704327981, 
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-2.52086075001167, 0.0287667805602271, 0.731343831738835, -0.451224270663529
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2.09179105565111, 0.116819106946508, 0.51447781508645, 1.87648378755979, 
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0.874359244531183, -2.35192438381252, -0.200559130397419, -1.29305021151605, 
-0.216777649470054, -1.43207151780606, -0.392317470556723, 0.447601162558867, 
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0.0822063408722808, 0.393896776121708, 0.968441995451939, -0.625513747288306, 
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0.167015138770557, 0.258108289841612, 0.198988855325523), V20 = c(-0.6885610185506, 
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4, 1, -1, -1, -3, -1, -3, 3, 2, -2, -3, 1, 0, 0, 0, 3, 0, 2, 
2, 1, -3, 1, 0)), class = "data.frame", row.names = c(NA, -24L
)) 

What is the best cross-validation strategy on this dataset when looking for the best model selection. I tried the leave-one-out cross-validation but it was hard to implement. The validation set approach, leave-one-out cross-validation, k-fold cross-validation, train-validation-test set approach, and nested k-fold cross-validation(What k value) are all known options to me, but I have a hard time making a good selection?