If you read the post, you can see that the biggest problem with the choice is that, unless your data is regularized, you will train a model that probably under performs: you are unnecessarily penalizing it by making it learn less than what it could from the data.
The second problem with the default behavior of
LogisticRegressionis about choosing a regularization constant that is — in effect — a magic number (equal to
1.0). This hides the fact that the regularization constant should be tuned by hyperparameter search, and not set in advance without knowing how the data and problem looks like.
Knowledge is power. Also read the post Giovanni links to in order to learn more about the issue.