Early stopping is a widely used technique in machine learning and deep learning that helps prevent overfitting during model training. It involves monitoring the model’s performance on a validation dataset and halting the training process when performance ceases to improve. By doing so, early stopping ensures that the model does not learn noise from the training data, which can lead to poor generalization on unseen data. This method not only saves computational resources but also reduces the time spent waiting for models to train, making it a valuable strategy in various applications, particularly in gradient-boosted decision trees and neural networks.