In Spark, leveraging in-memory computation is a key feature for optimizing performance. This technique involves caching or persisting intermediate data in memory, which is especially beneficial for iterative machine learning algorithms. By storing data in memory, Spark minimizes the need for disk I/O, leading to faster execution times for tasks that require multiple iterations over the same dataset.