Is there ever a good reason to use Insertion Sort?

An important concept in analysis of algorithms is asymptotic analysis. In the case of two algorithms with different asymptotic running times, such as one O(n^2) and one O(nlogn) as is the case with insertion sort and quicksort respectively, it is not definite that one is faster than the other.

The important distinction with this sort of analysis is that for sufficiently large N, one algorithm will be faster than another. When analyzing an algorithm down to a term like O(nlogn), you drop constants. When realistically analyzing the running of an algorithm, those constants will be important only for situations of small n.

So what does this mean? That means for certain small n, some algorithms are faster. This article from EmbeddedGurus.net includes an interesting perspective on choosing different sorting algorithms in the case of a limited space (16k) and limited memory system. Of course, the article references only sorting a list of 20 integers, so larger orders of n is irrelevant. Shorter code and less memory consumption (as well as avoiding recursion) were ultimately more important decisions.

Insertion sort has low overhead, it can be written fairly succinctly, and it has several two key benefits: it is stable, and it has a fairly fast running case when the input is nearly sorted.

From http://www.sorting-algorithms.com/insertion-sort:

Although it is one of the elementary sorting algorithms with O(n²) worst-case time, insertion sort is the algorithm of choice either when the data is nearly sorted (because it is adaptive) or when the problem size is small (because it has low overhead).

For these reasons, and because it is also stable, insertion sort is often used as the recursive base case (when the problem size is small) for higher overhead divide-and-conquer sorting algorithms, such as merge sort or quick sort.