Learning Rate Scheduling is a technique used in training machine learning models to adjust the learning rate over time. The learning rate determines how much to change the model's parameters in response to the estimated error each time the model is updated. By scheduling the learning rate, it can start high to allow for rapid learning and then decrease gradually to fine-tune the model, improving convergence and performance. There are various strategies for Learning Rate Scheduling, such as Step Decay, where the learning rate is reduced at specific intervals, or Exponential Decay, which decreases the rate continuously. These methods help prevent overshooting the optimal solution and can lead to better overall results in model training.