In general, the lower the p-value, the less belief you attach to your null hypothesis (in fact, the p-value is the chance that, if the null hypothesis were true, a test statistic so extreme (or more) as the one obtained from your sample would be obtained).
As such, it is reasonable to say that the lower the p-value, the more confident you are that there may be an alternative out there that is more probable to give this extreme statistic. As we are typically aiming to dis"prove" the null hypothesis (e.g. show that a coefficient in a regression is not zero), typically we say that lower p-values imply better results.
With the K-S test, it's a bit different: in fact, here, we typically hope that the null hypothesis is true. Therein lies the problem: at "best" we can say there is overwhelming evidence that the null hypothesis is not true (when the p-value is really low), or that the test we used did not provide evidence against the null hypothesis (e.g. if you find a p-value of 0.5). Unfortunately, there is nothing to say that there isn't an alternative out there (for K-S it could be e.g. the T-distribution instead of normal) that would give even better results!
In this manner, it is not a good idea to call the higher p-value "a better result". At most you could say that there is "less evidence against" its null hypothesis.
If there is some sound reason for applying the hard threshold of 5% (which in truth generally is rather arbitrary), it doesn't matter anyway, like you indicate.