Kernel Canonical Correlation Analysis (KCCA) is an extension of Canonical Correlation Analysis that uses kernel methods to map data into high-dimensional feature spaces, allowing it to capture nonlinear relationships between multivariate datasets. It is widely used in machine learning, pattern recognition, and data mining to extract meaningful patterns that are not evident in original space.