Difference between sklearn’s “log_loss” and “LogisticRegression”?
The metrics module by Scikit-learn implements functions assessing prediction error for specific purposes (Regression, classification, etc.). Model is the algorithm that actually does the classification/regression/clustering (as per need) for you.

Log-loss measures the accuracy of, say a classifier. It is used when the model outputs a probability for each class, rather than just the most likely class.

EDIT: Thanks to @mapto for suggesting documentation reference:


sklearn.metrics.log_loss,
and
sklearn.linear_model.LogisticRegression