Resampling is a method for ensuring that a machine learning model generalizes well to new data. It involves repeatedly drawing samples from the training data and evaluating the model on these samples to estimate its performance. Common resampling techniques include cross-validation, where the data is divided into k subsets and the model is trained and tested k times, each time using a different subset as the test set, and bootstrap, which involves randomly sampling with replacement to create multiple training sets. Resampling helps in assessing the model's stability and reliability, reducing overfitting, and selecting the best model.