Seasonal decomposition is a statistical method used to separate a time series into its seasonal, trend, and residual components to better understand underlying patterns and make more accurate forecasts. By isolating these components, analysts can identify recurring patterns and long-term trends, enhancing the interpretability and predictability of the data.