Gradient descent is the optimization algorithm that minimizes a model's loss function by iteratively adjusting weights in the direction that reduces error the most. Think of a hiker in foggy mountains trying to reach the lowest valley: at each step, they feel the slope beneath their feet and walk downhill. The learning rate controls step size. Variants like stochastic gradient descent (SGD) and Adam adapt the process for efficiency. All neural network training ultimately relies on gradient descent.