When model accuracy is near 0.5

Toss a Coin

More generally, when the accuracy of a classifier is too close to random classification(which is marked at 0.5 — like the toss of a coin), it probably means that something went wrong: features are not helpful, a hyper parameter is not correctly tuned, the classifier is suffering from class imbalance, etc…

And thus, implies a lot more work will need to be done to squeeze relevance out of our model, otherwise it is discarded. [But not always]