Early stopping is a regularization technique used in machine learning and specifically in the context of training iterative models, such as neural networks. The primary goal of early stopping is to prevent overfitting and improve the generalization of the model.

In the training process, a machine learning model is typically trained on a dataset for multiple epochs (iterations through the entire dataset). However, as the model continues to learn and adapt to the training data, there is a risk of overfitting, where the model starts to perform well on the training data but fails to generalize to new, unseen data.

Early stopping addresses this issue by monitoring the model's performance on a separate validation dataset during training. The validation dataset is not used for training the model but serves as an independent set to evaluate how well the model generalizes. The training process is stopped early (hence the name "early stopping") if the performance on the validation set starts to degrade or plateau.

The criteria for stopping can vary and may include metrics such as accuracy, loss, or other relevant performance indicators. For example, training might be halted if the validation loss stops decreasing or if it starts to increase after reaching a minimum.

By using early stopping, machine learning practitioners aim to find a balance between training a model long enough to learn from the data but stopping before it becomes too specialized to the training set, leading to poor generalization on new data.