Apply cross validation on regression algorithms How can we apply K-fold cross validation on say linear regression? Regression contains weight update and iterations, and so do we integrate K-fold to it?

If done, is it like we take the first K-1 fold to train using linear regression and perform iterations to obtain weight, then take the next fold and proceed the same and so on until all folds are done and pick the best weight or fold from it?
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.