Folds in Cross validation I am performing 10-folds cross-validation to evaluate the performances of a series of models (variable selection + regression) with R. I created manually the folds with this code. 

At the moment I'm performing first variable selection, then hyperparameters tuning through cv, and finally testing the performance with RMSE and MAE for all the models, but I have a doubt.

Is it correct to "use" the same fold for all the models? Or should I do a separate cv for each model?
I recommend trying both (more than once), and exploring any differences. In my experience, using the same set of folds for all models or using a new set of folds for each model doesn't make any material difference. Post if you find different!

Regarding "I'm performing first variable selection, then hyperparameters tuning through cv", maybe watch https://www.youtube.com/watch?reload=9&amp;v=S06JpVoNaA0 to be sure you are not introducing any bias.