A Support Vector Machine (SVM) is a very powerful and versatile Machine Learning model, capable of performing linear or nonlinear classification, regression, and even outlier detection.
In fact, I must say it is one of the most popular models in Machine Learning, they are well suited for classification of complex but small- or medium-sized datasets.
The other two models work perfectly on this training set, but their decision boundaries come so close to the instances that these models will probably not perform as well on new instances.
In contrast, the solid line in the plot on the right represents the decision boundary of an SVM classifier; this line not only separates the two classes but also stays as far away from the closest training instances as possible.
You can think of an SVM classifier as fitting the widest possible street (represented by the parallel dashed lines) between the classes.
This is called large margin classification.