# Why use cross-validation to do hyperparameter tuning (instead of train/val/test split)?

I understand that to get an unbiased estimate of performance you need some sort of outer CV. However, this assumes you already have dealt with hyperparameter tuning.

Nested CV suggests CV for the performance estimation in the outer loop, and CV for model selection/hyperparam tuning in the inner loop. I'm confused why an inner CV is necessary at all for hyperparameter tuning. Couldn't you do an inner train/val/test split? What do you gain by using CV for model selection? It just seems more computationally expensive.

[edit to clarify]: I mean it seems overkill to use nested k-folds, rather than just an outer k-folds and an inner train/val/test split.

• Train/validation/test split also is a form of cross-validation, same as $k$-fold cross-validation. – Tim Feb 10 '20 at 20:58
• Thank you for the clarification! I've regarded them as kind-of different things (considering train/val/test doesn't technically use the whole dataset for evaluation). I should have clarified that I meant more computationally expensive forms such as k-folds. – davzaman Feb 11 '20 at 3:01