I am a stats guy, and sklearn did confuse the hell out of me when I tried to use it as such, but I have to agree with others here. The issue is not how regression works, the issue is that sklearn does not do stats at all!
The use case you think about is not what sklearn does. You cant even use it, because it returns no model metrics you need for a specified model structure.
No one uses sklearn for stats, and logistic regression here is really not stats, it is a not a ML estimated conditional expectation, it is simply a consequence of a ML loss function. Ane in that sense, regularization is useful.
People expecting to get a stats model out of sklearn will fail immediately, since it does not even return errors, covariances or statistics.
Yeah but logistic regression is from stats. If you're going to implement ClassicalThingFromNearbyField in an ML library, people are right to complain when the definition doesn't match. Not surprising anyone, there's a lot of overlap between stats and ML.
If I implemented a "SimpleFraction" model in sklearn, it would be useful to use "[# this class] / ([# all classes] + epsilon)" using epsilon to regularize, but that would be a crazy name. Or at least it would be crazy if I used a default other than epsilon=0.