MLs are good at representing and manipulating complicated data with intricate invariants (e.g. using algebraic data types for representions, pattern-matching for traversing/manipulating data, abstract/existential types for encapsulation and enforcing interfaces, etc.). In practice this makes it good for handling other languages (after all, "ML" stands for "meta language").
Its original purpose was manipulating mathematical proofs/expressions, and it's still popular for that, e.g. Isabelle's core is written in PolyML, Coq is written in Ocaml, and I think some others like the various HOLs use it too; note that these see a little use in industry for verification (although some famous verification examples, like AMD's FPU verification, used ACL2 which is based on Lisp rather than ML).
It's also good for manipulating Web languages like HTML and Javascript, which has spawned a few compile-to-Web languages which are ML-like or implemented in ML, e.g. http://opalang.org and http://www.impredicative.com/ur . This might also be why Facebook made/use Reason ( https://reasonml.github.io ), which AFAIK is an alternative syntax for Ocaml.
Jane Street seem to be heavy users of Ocaml, since they sponsor a lot of work on the compiler and infrastructure.
The wiki page for SML claims it's used by chip manufacturers like ARM, but no citation.
However, languages like Ocaml and F# are direct descendants of ML, and those languages see wider modern adoption. In this sense ML is kind of equivalent to C, and something like Ocaml is kind of equivalent to C++. Haskell is also in the ML family but its laziness and emphasis on purity keeps it a bit separate from its cousins.
In general, FP sees adoption where correctness is of overriding importance -- e.g., Jane Street's use of Ocaml.
ML is a general-purpose programming language, and as such its use case cannot be narrowly specified. (They say it's good for writing compilers, but I wouldn't latch on that.)
