What is the significance of the 'learning rate' in model training?

The 'learning rate' is a critical hyperparameter in model training that dictates the speed at which a model updates its weights during the optimization process. It essentially controls how much to change the model weights in response to the estimated error each time the model weights are updated. A higher learning rate means the model will learn faster, as it makes larger updates to the weights; however, this can also risk overshooting the optimal solution. Conversely, a lower learning rate leads to smaller weight updates, which can make learning more stable, but can also result in longer training times and potential difficulties in converging to the optimal solution. Therefore, the learning rate is significant because it directly impacts the model's ability to learn efficiently and achieve good performance on the task at hand.