Kernel initialization in machine learning refers to the process of setting the initial values of the parameters of a kernel function, which can significantly influence the convergence and performance of models like support vector machines and Gaussian processes. Proper initialization helps ensure that the optimization process starts from a good point in the parameter space, potentially leading to faster convergence and better model accuracy.