How cross validation works for regression?
The idea behind cross validation is to understand the performance of some measure of your model's performance on unseen data. This can be applied to loads of different statistics, not just ones relevant to classification like accuracy. Common measures for holdout performance of regression models include MSE and MAPE, either (or both) of which can be cross-validated over.