Yesterday we saw how leakage ruins validation. Today is the mirror image — designing validation without leakage to honestly estimate model generalization. Good model selection ultimately comes down to reliably estimating "how well will it perform on unseen data?" and that reliability depends entirely on validation design.
MLS-C01 repeatedly asks validation strategy selection. When data is small, when classes are imbalanced, when it's time series — what do we use? Today covers 3-way split, k-fold cross-validation, stratified sampling, and time series split.
The most basic is splitting data into three parts