Does it make sense to apply train-test split or k-fold cross-validation to a simple linear regression model or multiple linear regression model?
I'm really confused about this because I saw this question: How to Evaluate Results of Linear Regression, where the upvoted comments and answers suggest no.
Comment by @octern:
I don't think this kind of assessment is generally used with simple regression models. What would it tell you that you wouldn't find out from using the entire dataset to generate your regression parameters? Normally the reason to use an evaluation dataset is to prevent overfitting, but that's not an issue when you already know that your model is going to contain just one independent variable.
Top answer by @MattKrause:
I'd agree with @Octern that one rarely sees people using train/test splits (or even things like cross-validation) for linear models. Overfitting is (almost) certainly not an issue with a very simple model like this one.