# Feature selection in scikit-learn and multilinear regression

I have a process with ~10 features and ~100 responses, and would like to search for models for how those features interact to create various responses. ~100 experiments were run, exploring combinations of values of the features, so it’s a limited training set. I was thinking about doing multilinear regression (possibly exploring quadratic terms) but I'm not sure what's the most elegant/simple way is, in scikit-learn, to explore all possible model parameters to find the most convincing models.

If you have experience with this sort of problem (seems like a standard subset selection problem?) in scikit-learn or statsmodels, please give me some pointers. And please let me know if my question is unclear.

And what do you mean by $O(10^2)$ "experiments" were run ..., How do you define "experiments"? is it number of predictions?