Linear Regression + Cross Validation model training with sklearn

Question

I am new in python sklearn. I understand the basic of cross-validation. If I split the data to 3 folds by default. sklearn will train the model 3 times with different training and testing sets of data. I assume it produces 3 different model, i mean different w^ and d^. Is this right? Should I just get 1 model back? If i use model.predict() to predict an input, which model i am using?

Answer 1

Cross validation evaluates model setup, not model parameters.

Ie if I use a bad setup, like a LR with 20 parameters over 10 data points, cross validation will report low scores because the model in this setup does not generalize, not because model(s) parameters were wrong.

If after cross validation you conclude the model generalizes well, all trained models will be pretty similar. It is safe to use any of them or even get the final model by training over the entire dev dataset.