When using the MEMORY_AND_DISK storage level, Spark attempts to cache as much of the DataFrame in memory as possible. If the DataFrame does not fit entirely in memory, Spark will store the remaining partitions on disk. This allows processing to continue, albeit with a performance overhead due to disk I/O.

As per the Spark documentation:

'MEMORY_AND_DISK: It stores partitions that do not fit in memory on disk and keeps the rest in memory. This can be useful when working with datasets that are larger than the available memory.'

--- Perficient Blogs: Spark - StorageLevel

This behavior ensures that Spark can handle datasets larger than the available memory by spilling excess data to disk, thus preventing job failures due to memory constraints.