Learning rates play a vital role in training neural networks, affecting convergence speed and stability. Proper tuning can prevent suboptimal solutions or prolonged training. Techniques like adaptive learning rates, warm-up phases, and learning rate finders aid in optimizing this critical hyperparameter for better model performance.