Gradient descent is an optimization method that iteratively adjusts model parameters in the direction that reduces a loss function. It uses gradients to determine how weights and biases should change during training.
A mathematical technique to minimize loss. Gradient descent iteratively adjusts weights and biases, gradually finding the best combination to minimize loss. Gradient descent is older---much, much older---than machine learning. See the Linear regression: Gradient descent in Machine Learning Crash Course for more information.