O(log n) would basically be if each iteration you are dividing the remaining elements in half (i.e.- traversing a binary search tree). So an example of something that would look like O(log n) in a for loop could be:
for(int i = 1; i <= n; i = i * 2)
if n was 32, i would progress as follows: 1, 2, 4, 8, 16, 32. Notice if there were 32 elements, it only took until the 5th iteration to get to 32. log (base 2) 32 = 5 or 2^5 = 32. Hence it is O(log n). Now this could change depending on what was in place of //some code. For instance, inside the loop, if you looked through the entire set of n elements (or had the potential to look through them all, say in a linear search) then you could say O(n log n). This was a very basic explanation but hopefully it makes at least O(log n) a little easier to understand.