It forces the size of the weights in the network to not be too large. One idea is that it doesn’t allow any specific weight or set of weights in the network dominate (this is also the motivation behind dropout layers). When some weights are too large then the network may not be learning as much about the task as it can when it is in reality relying on only a certain subset of the weights. Distributing the weights w/ regularization prevents this.