Multi-scale feature learning involves processing data at various levels of granularity to capture patterns that are apparent at different scales, allowing models to effectively recognize both large structures and subtle details. This approach optimizes the extraction of informative features, significantly enhancing the performance of tasks such as image analysis and natural language processing.