Several thoughts:

* OVL was used often in the finance firm where I worked.
* It provides a simple, easy to understand point estimate
  of the similarity or overlap between two PDFs.
* It was far easier to use than a Students-t test to answer
  the question of how similar two normal distributions
  are and it is more precise than the more common technique
  of just running overlapping plots and doing it by eye.
* It isn't easy for end-users to do this themselves
  without running an integration.
* It is well defined and well motivated:  

  See: "The overlapping coefficient as a measure of agreement
  between probability distributions and point estimation of the
  overlap of two normal densities" -- Henry F. Inman and 
  Edwin L. Bradley Jr
  http://dx.doi.org/10.1080/03610928908830127

  See also: https://www.rasch.org/rmt/rmt101r.htm

  And: http://www.iceaaonline.com/ready/wp-content/uploads/2014/06/MM-9-Presentation-Meet-the-Overlapping-Coefficient-A-Measure-for-Elevator-Speeches.pdf

Perhaps, the wording can be improved on the male/female height example.  Measured to finite precision, perhaps to the nearest centimeter, there will be overlaps.  This is same kind of binning done with chi-square tests to compare how well two distributions match.

AFAICT, this tool is well-defined, tested, and has legitimate use cases that are easy to achieve in other ways using only standard library tools.