Early stopping is a technique in machine learning that involves monitoring a model's performance during training and halting the training process once the model reaches its peak performance, rather than letting it run to completion. Various heuristics can be employed to identify when the model has achieved a local maximum in performance. By stopping training early, this method helps prevent overfitting and avoids the unnecessary consumption of time and computational resources.