Question about Knn and split validation
There is generally a trade of in k-NN:


k should be large enough to cancel out any noise.
k shouldn't be too large to create big biased boundaries.


One rule of thumb is to select an odd k value to avoid ties in binary class problems.
Small and big value of k is subjective to the underlying structure of the data set itself, that's why we check over a range of k values. 
Both of the above gets settled if done a good job with cross validation.
So the most important question you need to focus is:

Have you chosen the right metric for model selection, keeping the imbalance of classes into consideration?

I would recommend you to first identify the class that is more important for you to predict correctly. Then, look at the confusion matrix and ponder upon different metrics (eg. recall, precision etc.) before finalizing a model/ k value.