Not really. Maybe there are some situations where you could use both, but the main idea is the following:
In nn.Sequential, the nn.Module's stored inside are connected in a cascaded way. For instance, in the example that I gave, I define a neural network  that receives as input an image with 3 channels and outputs 10 neurons. That network is composed by the following blocks, in the following order: Conv2D -> ReLU -> Linear layer. Moreover, an object of type nn.Sequential has a forward() method, so if I have an input image x I can directly call y = simple_cnn(x) to obtain the scores for x. When you define an nn.Sequential you must be careful to make sure that the output size of a block matches the input size of the following block. Basically, it behaves just like a nn.Module
On the other hand, nn.ModuleList does not have a forward() method, because it does not define any neural network, that is, there is no connection between each of the nn.Module's that it stores. You may use it to store nn.Module's, just like you use Python lists to store other types of objects (integers, strings, etc). The advantage of using nn.ModuleList's instead of using conventional Python lists to store nn.Module's is that Pytorch is “aware” of the existence of the nn.Module's inside an nn.ModuleList, which is not the case for Python lists. If you want to understand exactly what I mean, just try to redefine my class LinearNet using a Python list instead of a nn.ModuleList and train it. When defining the optimizer() for that net, you’ll get an error saying that your model has no parameters, because PyTorch does not see the parameters of the layers stored in a Python list. If you use a nn.ModuleList instead, you’ll get no error.