That's definitely not what they're most useful for. I mean, you probably can use a bloom filter for implementing spell check, but saying that's where they're most useful severely misses the point of probabilistic set membership queries.
Bloom filters and their relatives are great when you have a huge set of values – eg. 100s of millions of user IDs in some database – and you want to have a very fast way of checking whether some value might be in that set, without having to query the database. Naturally this assumes that you've prepopulated a bloom filter with whatever values you need to be checking.
If the result of the bloom filter query is "nope", you know that the value's definitely not in the set, but if the result is "maybe" then you can go ahead and double-check by querying the database. This means that the vast majority of checks don't have to hit that slow DB at all, and even though you'll get some false positives this'll still be much much much faster than having to go through that DB every time.