The Kernel Trick lets SVMs learn nonlinear boundaries by computing pairwise similarity in a high-dimensional (possibly infinite) space. It avoids the explicit computation of feature mappings, reducing computational complexity. This trick is central to many kernel-based algorithms like Kernel PCA.