Gradient descent is an optimization algorithm for finding a local minimum of a differentiable function. Used extensively in machine learning, it fine-tunes model parameters by moving in the opposite direction of the gradient for minimizing a cost function. The learning rate determines step size; too large, and minima will be skipped, too small, and the process becomes slow.

For linear regression, gradient descent optimizes coefficients by reducing error between predicted and actual values. It's crucial to normalize input variables to ensure consistent learning rates across datasets.

For logistic regression, gradient descent handles classification issues; here, the cost function, Binary Cross Entropy, drives adjustments.

Understanding gradient descent is necessary for effective implementation in various machine learning models.

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