Learning Rate Annealing is a technique used in machine learning to adjust the learning rate during the training process. The learning rate determines how much to change the model's parameters in response to the estimated error each time the model weights are updated. By gradually decreasing the learning rate, the model can converge more effectively, allowing for finer adjustments as it approaches an optimal solution. This method helps prevent overshooting the minimum of the loss function, which can occur with a high learning rate. Learning Rate Annealing can be implemented in various ways, such as using a schedule or adapting the rate based on performance metrics during training.