How does Gradient Descent work in Scikit-Learn linear models?

Gradient descent iteratively updates parameters to minimize loss.

In models like SGDClassifier and SGDRegressor, weights are updated using gradients of loss functions. Each iteration adjusts coefficients in direction of steepest descent. Learning rate controls step size, affecting convergence speed and stability.