I think I understand completely the concept of cross validation, but there is one aspect I've never seen detailed. Let's assume I have a logistic regression model with four parameters I want to train. I perform k-fold cross validation with k, let's say, 5, over my training data, and it yields 5 different sets of four values with 5 different associated error values. How should I then select which model to use? A weighted combination of the five models? The best one? What is the standard approach to do so and (if there is any) what are its mathematical foundations?
Thank you very much in advance.