The train-test split is a method used in machine learning to divide a dataset into two parts: a training set and a test set.

The training set is used to train a model, while the test set is used to evaluate the model's performance. This split is crucial as it allows the model to be tested on unseen data, helping to prevent overfitting.

Overfitting occurs when a model learns the training data too well, performing poorly on unseen data. Evaluating the model on a test set provides a better indication of how it will perform in real-world scenarios.

Additionally, this approach helps to understand the model's generalization ability and allows for the tuning of hyperparameters by comparing performance across different test sets.