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.
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.
scikit-learn is an ML library. It's explicitly not a stats library. The assumptions and defaults are correct for ML use cases. GP comment is entirely correct.
If you want to do widespread statistics, use StatsModels which is designed for that.