Splitting data into training and testing sets gives a fair measure of how well a machine learning model works—just as reliable as more complex methods—because it keeps the test data completely separate so the model doesn't cheat by seeing it early.

Nested cross-validation is like having two layers of checkups when testing a model—it keeps the test data totally separate so the model doesn’t cheat, giving a fairer score no matter how much data you have.

If you're testing a machine learning model using a common method called K-fold cross-validation, you might think it's working better than it really is—especially if you're tuning the model using all your data first. This can trick you into believing your model is accurate when it won't work as well on new data.