I'm developing two k-fold cross-validated models, based on two different data sets, but using the same variables. I plan to then apply both models to each data set and calculate a few model performance measures.
The goals is to purposefully compare how applying the model created from data set 1 to data set 2 is related to a potential decrease in performance against applying the model created from data set 2 to the data in data set 2.
Are there any further steps that must be taken to ensure methodological validity? For example, is there a need to apply the k-fold to a reduced sample and compare the performance of the models applied to the remaining sample data?