Gradient descent allows us to explore the continuous parameter space rather than in discrete step size.

Gradient descent always gurantees in finding the global minimum, while grid search may get stuck in a local minima.

This is because the derivative gives the direction of steepest ascent, allowing us to more efficiently find the optimial solution in a large and complex search space.