Gaussian process kernels have hyperparameters like length scales, variance, and smoothness that influence model flexibility and prediction accuracy. Optimizing these via log-marginal likelihood tuning in scikit-learn’s GaussianProcessRegressor adapts kernels to data structure, noise, and function smoothness for improved performance.