Apply cross validation on regression algorithms
How to use cross-validation on regression (assuming 10-fold for example purposes): separate your dataset in 10% and 90%, train on 90%, test your metric (squared error or anything you're modeling) on the remaining 10%. Do that 10 times using different 10% groups. Now you have 10 of your metrics, you can analyze its mean and range to see if the model is robust against over-fitting.

How do you pick a model: you train on the whole dataset. Cross-validation is for testing/validating purposes, you don't use the models generated by cross-validation.