We use caching and persisting to improve performance and execution time. When a dataset is used multiple times, these methods help Spark avoid recomputing the same data repeatedly. This is especially useful in complex transformation pipelines. While cache() uses the default storage level, persist() allows specifying custom storage levels (like disk-only, memory-only, etc.).