This formula then says (if I'm interpreting it correctly) that less than one "valid" key exists. What does it mean practically?
The explanation of the Wikipedia article must be understood in an "average" sense.
And the average is taken over all the encripted texts (that is, with inputs corresponding to all the $N$ plaintexts, not only from the $M$ legit -"readable"- ones).
Then, it's clear that as $K M/N \ll 1$, if we take a random encrypted text the average number of keys that will "work" will be less than one - or the probability that some key will work will be low.
Now, you might object: but, in the typical brute-force atttack scenario, don't we already know that the encrypted text corresponds to a legit plaintext? Don't we already know that a key works, and we are worried about extra -spurious- working keys? Well, it doesn't change much the scenario - or put in other way, to ask when the global average of working keys is about one is roughly equivalent to ask when the average number of spurious keys is significant.
If we restrict the average to the legit encrypted messages, of course the mean working keys will be greater than one. Here's a possible approach: assuming the coding-decoding with a fixed key can be modelled as a uniformly random one-to-one mapping, the probability that a random key for a random encrypted message "works" (gives a readable message) is $M/N$, hence $T=$ the number of working keys is roughly a Binomial $B(M/N,K)$ with mean $\mu=KM/N$. For small $M/N$ and large $K$, this can be approximated by a Poisson of mean $\mu$.
If we are insterested only in the encripted message produced by a legit plaintext, then we are interested in the Trucanted Poisson. Specifically, we are interested in the probability
$$P(T>1 \mid T>0)\approx \frac{e^\mu-1-\mu}{e^\mu -1}$$
We see that for $\mu=1$ the probability is $(e-2)/(e-1)\approx 0.42$
Because we are just interested in an order-of-magnitude value, this seems reasonable.

The graph above show the probability of spurious keys (in the model above) as a function of $\mu=K M/N$ (log scale). Again, $\mu=1$ looks as a fair threshold.