Early stopping is a technique used in machine learning to prevent overfitting by terminating the training process before it worsens. In TensorFlow 2, early stopping can be implemented using the `tf.keras.EarlyStopping` callback function, which monitors a specific value such as validation accuracy and stops training when that value has improved for a certain number of epochs, known as patience. The threshold for improvement is controlled by the `min_delta` argument, and the maximum number of epochs is set to prevent overfitting. When early stopping is triggered, the training process terminates at the epoch where improvements on the validation accuracy stop, despite reaching the maximum number of epochs. This technique helps improve generalization performance by preventing the model from overfitting to the training data.