The Radial Basis Function (RBF) kernel is primarily used in Support Vector Machines (SVM) when the dataset is linearly inseparable. The classification performance of an RBF kernel SVM is highly sensitive to its hyperparameters: the regularization parameter $C$ and the kernel parameter $\gamma$. These hyperparameters are typically tuned using cross-validation on the training data. An RBF kernel SVM is particularly effective when the number of features is relatively small and the number of training samples is moderate.