The Kernel Trick is a clever mathematical technique used in machine learning, particularly in algorithms like Support Vector Machines (SVM). It allows us to transform data into a higher-dimensional space without explicitly calculating the coordinates of the data points in that space. This transformation helps in making complex patterns more separable, enabling better classification. By using a kernel function, we can compute the relationships between data points in this higher-dimensional space efficiently. Common kernel functions include the Gaussian (or RBF) kernel and the polynomial kernel. This approach saves time and computational resources while still achieving powerful results in tasks like classification and regression.