Transferring knowledge from one field to another
Computer Science has many subfields, I remember that there was a running gag in NLP that people just took algorithms from the 1980's and pushed papers out like a treadmill. 

Other fields like Statistical Machine Learning has a more math oriented approach and you do have to come up with novel stuff most of the time.

However, when you do interdisciplinary research, it is very often that many good techniques just don't get tested because many people do not know how to use them, or do not have the time to understand them very well. 

But to answer your question, I think is a pretty valid practice, very good researchers have great papers where what they did was basically apply a widely known math tool to a novel problem that few people were working on.

An example that comes to mind is how Factor Analysis, a widely known CS method (1993) was just applied in 2007 for the problem of discovering Connections in Gene Networks, not only that, people were using linear regression on that just 10 years ago.