The Kernel Trick allows algorithms to operate in a high-dimensional space without explicitly computing the coordinates of the data in that space, enabling efficient computation of linear separations in transformed feature spaces. This is particularly useful in support vector machines and other algorithms that rely on inner products, as it allows them to handle non-linear relationships by implicitly mapping inputs into higher dimensions.