I would first like to create few multiple regression models based on if the models violate any multiple regression assumptions and how well it fits the training data. Then I would like to compare how well these models predict new observations by using 5-fold Cross Validation. From my understanding 5-fold Cross Validation shuffles then splits my data into 5 groups and chooses 1 for the testing set, and the other 4 for the training set. A given model is tested and the prediction error is recorded. This is repeated until all 5 groups are used as a testing set. Finally, the prediction errors are averaged.
My question is, when I am first determining the multiple regression equation (checking for assumptions, applying transformations, variable selection, etc.) which set of data should I use as my training set? Do I use the entire data set? Do I use one of the 5 training sets created by the 5-folds CV method? Do I repeatedly try to fit the regression model for all 5 training sets? If so, how would I extract each training set using the caret package?