The computationally expensive default option when your model doesn’t have any obvious short cuts for complexity regularization, for example when AIC cannot be shown to work.
To learn: how this interacts with Bayesian inference.
Basic Cross Validation
Generalised Cross Validation
🏗 Hat matrix, smoother matrix. Note comparative computational efficiency. Define hat matrix.
Bayesian Cross validation
What even is cross validation?
But there is other stuff going on. Here is an interesting sampling of opinions: Rob Tibshirani, Yuling Yao, and Aki Vehtari on cross validation
Cross-methods such as cross-validation, and cross-prediction are effective tools for many machine learning, statistics, and data science related applications. They are useful for parameter selection, model selection, impact/target encoding of high cardinality variables, stacking models, and super learning. As cross-methods simulate access to an out of sample data set the same the original data, they are more statistically efficient, lower variance, than partitioning training data into calibration/training/holdout sets. However, cross-methods do not satisfy the full exchangeability conditions that full hold-out methods have. This introduces some additional statistical trade-offs when using cross-methods, beyond the obvious increases in computational cost.
Specifically, cross-methods can introduce an information leak into the modeling process.