In the social science context where I come from, the issue is whether you are interested in (a) prediction or (b) testing a focused research question.
If the purpose is prediction then data driven approaches are appropriate.
If the purpose is to examine a focused research question then it is important to consider which regression model specifically tests your question.
For example, if your task was to select a set of selection tests to predict job performance, the aim can in some sense be seen as one of maximising prediction of job performance.
Thus, data driven approaches would be useful.
In contrast if you wanted to understand the relative role of personality variables and ability variables in influencing performance, then a specific model comparison approach might be more appropriate.
Typically when exploring focussed research questions the aim is to elucidate something about the underlying causal processes that are operating as opposed to developing a model with optimal prediction.
When I'm in the process of developing models about process based on cross-sectional data I'd be wary about:
(a) including predictors that could theoretically be thought of as consequences of the outcome variable. E.g., a person's belief that they are a good performer is a good predictor of job performance, but it is likely that this is at least partially caused by the fact that they have observed their own performance.
(b) including a large number of predictors that are all reflective of the same underlying phenomena. E.g., including 20 items all measuring satisfaction with life in different ways.
Thus, focused research questions rely a lot more on domain specific knowledge.
This probably goes some way to explaining why data driven approaches are less often used in the social sciences.