I find it depends on what you want. There is no canonical GAM (gen. addative model) library in python but there are a few options - which are not easy to use. The statsmodels GAM implementation appears to be broken. R, of course, has a stupid easy to use GAM library that is pretty fast.

On the other hand, R has too many obscure options for what I can find in scipy or sklearn. So I find it easier to just jump into sklearn, use the very nice unified interface "pipelines" to churn through a whole bunch of different estimators without having to do any munging on my data.

So I think it just depends on your field. But R seems to stick more with academia.