Question: What is cross-validation, and why is it important?

Answer: Cross-validation is a technique used to assess a model's performance by splitting the data into multiple subsets, training the model on some, and evaluating it on the others. It helps estimate how well a model will generalize to new data.

Example:

K-fold cross-validation divides data into k subsets; each subset is used for both training and validation in different iterations.