The learning rate is a crucial hyperparameter in machine learning that determines how much to adjust the model's weights during training. A higher learning rate can speed up training but may lead to overshooting the optimal solution, while a lower learning rate ensures more precise adjustments but can slow down the training process. Choosing the right learning rate is essential for effective model training. Techniques like learning rate schedules or adaptive learning rate methods can help optimize this parameter, allowing the model to learn efficiently over time while minimizing the risk of divergence or stagnation.