The basis of this rule of thumb is that having too few observations relative to the number of predictor variables can lead to unstable estimates of the model parameters, making it difficult to generalize to new data. In particular, if the number of observations is small relative to the number of predictor variables, the model may fit the noise in the data rather than the underlying signal, leading to overfitting.