Consider an alternative situation in which the data has 10 features, but you only make use of 1 feature. Your model hypothesis would still be the following:
However, that is the equation of a straight line, but your model is probably ignoring a lot of information. The model is over-simplified and is said to underfit the data.
The solution to underfitting is to provide the model with more features, or conversely, less data to train on; but more features is the better approach.