Spark caching in the context of a single job

Does caching benefit a spark job with only a single action in it? Spark IIRC already optimizes shuffles by persisting them onto the disk

I am unable to find a counter-example where caching would benefit a job with a single action. In every case I can think of, the shuffle checkpoint acts as a good enough caching mechanism in itself

FWIW, I am talking specifically in the context of the Dataframe API. The StorageLevel allowed in my case is DISK_ONLY i.e. I am not looking to speed up by caching data in memory

To rephrase, is DISK_ONLY caching better or same as shuffle checkpointing in the context of a single action