🔄 Why Cross Validation Is Better Than One Train/Test Split

Imagine flipping a coin 10 times. You might get 8 heads.
Does that mean the coin is biased?
Not necessarily.

A single train/test split can also give a misleading performance estimate.

Cross Validation repeats the process multiple times using different splits.
Instead of trusting one lucky result... You measure average performance across several experiments.

It's a much better estimate of how your model will perform on unseen data.