Canonical Correlation Analysis (CCA) is a multivariate statistical technique that explores the relationships between two sets of variables by finding linear combinations that maximize the correlation between the datasets. It is particularly useful in identifying and quantifying the shared information between the two variable sets, making it a powerful tool for data analysis in fields such as genomics, psychology, and machine learning.