Early stopping is a widely used technique in machine learning and deep learning to 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. This approach allows practitioners to specify a large number of training epochs while ensuring that the model does not learn noise from the training data. By stopping training at the right moment, early stopping helps maintain a balance between bias and variance, ultimately leading to better generalization on unseen data. This technique is particularly beneficial in scenarios with limited computational resources.